Overview

Dataset statistics

Number of variables9
Number of observations1029
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)1.1%
Total size in memory72.5 KiB
Average record size in memory72.1 B

Variable types

Numeric9

Alerts

Dataset has 11 (1.1%) duplicate rowsDuplicates
water is highly overall correlated with superplasticizerHigh correlation
superplasticizer is highly overall correlated with waterHigh correlation
age is highly overall correlated with strengthHigh correlation
strength is highly overall correlated with ageHigh correlation
slag has 470 (45.7%) zerosZeros
ash has 565 (54.9%) zerosZeros
superplasticizer has 378 (36.7%) zerosZeros

Reproduction

Analysis started2023-07-28 19:28:24.171257
Analysis finished2023-07-28 19:28:38.513333
Duration14.34 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

cement
Real number (ℝ)

Distinct278
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.19621
Minimum102
Maximum540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-07-29T00:58:38.789581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile143.74
Q1192
median273
Q3350
95-th percentile480
Maximum540
Range438
Interquartile range (IQR)158

Descriptive statistics

Standard deviation104.55322
Coefficient of variation (CV)0.37181589
Kurtosis-0.52324795
Mean281.19621
Median Absolute Deviation (MAD)79.5
Skewness0.50849864
Sum289350.9
Variance10931.376
MonotonicityNot monotonic
2023-07-29T00:58:39.096759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425 20
 
1.9%
362.600006 20
 
1.9%
251.399994 15
 
1.5%
310 14
 
1.4%
446 14
 
1.4%
250 13
 
1.3%
331 13
 
1.3%
475 13
 
1.3%
349 12
 
1.2%
387 12
 
1.2%
Other values (268) 883
85.8%
ValueCountFrequency (%)
102 4
0.4%
108.300003 4
0.4%
116 4
0.4%
122.599998 4
0.4%
132 2
 
0.2%
133 5
0.5%
133.100006 1
 
0.1%
134.699997 1
 
0.1%
135 2
 
0.2%
135.699997 2
 
0.2%
ValueCountFrequency (%)
540 9
0.9%
531.299988 5
0.5%
528 1
 
0.1%
525 7
0.7%
522 2
 
0.2%
520 2
 
0.2%
516 2
 
0.2%
505 1
 
0.1%
500.100006 1
 
0.1%
500 10
1.0%

slag
Real number (ℝ)

ZEROS 

Distinct185
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.967638
Minimum0
Maximum359.39999
Zeros470
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-07-29T00:58:39.321188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22
Q3143
95-th percentile236
Maximum359.39999
Range359.39999
Interquartile range (IQR)143

Descriptive statistics

Standard deviation86.290493
Coefficient of variation (CV)1.1665979
Kurtosis-0.51023251
Mean73.967638
Median Absolute Deviation (MAD)22
Skewness0.79929719
Sum76112.7
Variance7446.0491
MonotonicityNot monotonic
2023-07-29T00:58:39.526483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 470
45.7%
189 30
 
2.9%
106.300003 20
 
1.9%
24 14
 
1.4%
20 12
 
1.2%
145 11
 
1.1%
98.099998 10
 
1.0%
19 10
 
1.0%
26 8
 
0.8%
22 8
 
0.8%
Other values (175) 436
42.4%
ValueCountFrequency (%)
0 470
45.7%
11 4
 
0.4%
13.6 5
 
0.5%
15 5
 
0.5%
17.200001 1
 
0.1%
17.5 1
 
0.1%
17.6 1
 
0.1%
19 10
 
1.0%
20 12
 
1.2%
22 8
 
0.8%
ValueCountFrequency (%)
359.399994 2
 
0.2%
342.100006 2
 
0.2%
316.100006 2
 
0.2%
305.299988 4
0.4%
290.200012 2
 
0.2%
288 4
0.4%
282.799988 4
0.4%
272.799988 2
 
0.2%
262.200012 5
0.5%
260 1
 
0.1%

ash
Real number (ℝ)

ZEROS 

Distinct156
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.241011
Minimum0
Maximum200.10001
Zeros565
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-07-29T00:58:39.739911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3118.3
95-th percentile167
Maximum200.10001
Range200.10001
Interquartile range (IQR)118.3

Descriptive statistics

Standard deviation64.005792
Coefficient of variation (CV)1.1800258
Kurtosis-1.3303115
Mean54.241011
Median Absolute Deviation (MAD)0
Skewness0.53577448
Sum55814
Variance4096.7414
MonotonicityNot monotonic
2023-07-29T00:58:39.984292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 565
54.9%
118.300003 20
 
1.9%
141 16
 
1.6%
24.5 15
 
1.5%
79 14
 
1.4%
94 13
 
1.3%
100.400002 11
 
1.1%
167 10
 
1.0%
98.800003 10
 
1.0%
100.5 10
 
1.0%
Other values (146) 345
33.5%
ValueCountFrequency (%)
0 565
54.9%
24.5 15
 
1.5%
59 1
 
0.1%
60 1
 
0.1%
71 1
 
0.1%
71.5 1
 
0.1%
75.599998 1
 
0.1%
76 1
 
0.1%
77 2
 
0.2%
78 2
 
0.2%
ValueCountFrequency (%)
200.100006 1
 
0.1%
200 1
 
0.1%
195 3
0.3%
194.899994 1
 
0.1%
194 1
 
0.1%
193 1
 
0.1%
190 1
 
0.1%
187 1
 
0.1%
185.300003 1
 
0.1%
185 2
0.2%

water
Real number (ℝ)

HIGH CORRELATION 

Distinct195
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.56395
Minimum121.8
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-07-29T00:58:40.207694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum121.8
5-th percentile146.10001
Q1164.89999
median185
Q3192
95-th percentile228
Maximum247
Range125.2
Interquartile range (IQR)27.100006

Descriptive statistics

Standard deviation21.364334
Coefficient of variation (CV)0.11766837
Kurtosis0.11925177
Mean181.56395
Median Absolute Deviation (MAD)13
Skewness0.075060412
Sum186829.3
Variance456.43475
MonotonicityNot monotonic
2023-07-29T00:58:40.448863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192 118
 
11.5%
228 54
 
5.2%
185.699997 46
 
4.5%
203.5 36
 
3.5%
186 28
 
2.7%
164.899994 20
 
1.9%
162 20
 
1.9%
153.5 15
 
1.5%
200 14
 
1.4%
185 14
 
1.4%
Other values (185) 664
64.5%
ValueCountFrequency (%)
121.800003 5
0.5%
126.599998 5
0.5%
127 1
 
0.1%
127.300003 1
 
0.1%
137.800003 5
0.5%
140 1
 
0.1%
140.800003 5
0.5%
141.800003 5
0.5%
142 1
 
0.1%
143.300003 5
0.5%
ValueCountFrequency (%)
247 1
 
0.1%
246.899994 1
 
0.1%
237 1
 
0.1%
236.699997 1
 
0.1%
228 54
5.2%
221.399994 1
 
0.1%
221 2
 
0.2%
220.100006 1
 
0.1%
220 2
 
0.2%
219.699997 1
 
0.1%

superplasticizer
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.21069
Minimum0
Maximum32.200001
Zeros378
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-07-29T00:58:40.657303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.4
Q310.2
95-th percentile16.06
Maximum32.200001
Range32.200001
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation5.9736095
Coefficient of variation (CV)0.96182703
Kurtosis1.4114542
Mean6.21069
Median Absolute Deviation (MAD)5.3
Skewness0.90625068
Sum6390.8
Variance35.684011
MonotonicityNot monotonic
2023-07-29T00:58:40.876718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 378
36.7%
11.6 37
 
3.6%
8 27
 
2.6%
7 19
 
1.8%
6 17
 
1.7%
8.9 16
 
1.6%
9.9 16
 
1.6%
7.8 16
 
1.6%
9 16
 
1.6%
10 15
 
1.5%
Other values (101) 472
45.9%
ValueCountFrequency (%)
0 378
36.7%
1.7 4
 
0.4%
1.9 1
 
0.1%
2 1
 
0.1%
2.2 1
 
0.1%
2.5 2
 
0.2%
3 6
 
0.6%
3.1 1
 
0.1%
3.4 3
 
0.3%
3.6 5
 
0.5%
ValueCountFrequency (%)
32.200001 5
0.5%
28.200001 5
0.5%
23.4 5
0.5%
22.1 1
 
0.1%
22 6
0.6%
20.799999 1
 
0.1%
20 1
 
0.1%
19 1
 
0.1%
18.799999 1
 
0.1%
18.6 5
0.5%

coarseaggregate
Real number (ℝ)

Distinct284
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean972.78474
Minimum801
Maximum1145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-07-29T00:58:41.099869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum801
5-th percentile842
Q1932
median968
Q31029.4
95-th percentile1104
Maximum1145
Range344
Interquartile range (IQR)97.400024

Descriptive statistics

Standard deviation77.672347
Coefficient of variation (CV)0.079845359
Kurtosis-0.59658617
Mean972.78474
Median Absolute Deviation (MAD)46.299988
Skewness-0.040668902
Sum1000995.5
Variance6032.9935
MonotonicityNot monotonic
2023-07-29T00:58:41.318256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
932 57
 
5.5%
852.099976 45
 
4.4%
944.700012 30
 
2.9%
968 29
 
2.8%
1125 24
 
2.3%
967 19
 
1.8%
1047 19
 
1.8%
938 12
 
1.2%
974 12
 
1.2%
942 12
 
1.2%
Other values (274) 770
74.8%
ValueCountFrequency (%)
801 4
0.4%
801.099976 1
 
0.1%
801.400024 1
 
0.1%
811 2
0.2%
814 1
 
0.1%
814.099976 1
 
0.1%
817.900024 1
 
0.1%
818 1
 
0.1%
819 2
0.2%
819.200012 1
 
0.1%
ValueCountFrequency (%)
1145 1
 
0.1%
1134.300049 5
 
0.5%
1130 1
 
0.1%
1125 24
2.3%
1124.400024 2
 
0.2%
1120 2
 
0.2%
1119 2
 
0.2%
1118.800049 2
 
0.2%
1118 1
 
0.1%
1113 2
 
0.2%

fineaggregate
Real number (ℝ)

Distinct302
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean773.57036
Minimum594
Maximum992.59998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-07-29T00:58:41.534477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum594
5-th percentile613
Q1730.40002
median779.29999
Q3824
95-th percentile898.27999
Maximum992.59998
Range398.59998
Interquartile range (IQR)93.599976

Descriptive statistics

Standard deviation80.214308
Coefficient of variation (CV)0.10369362
Kurtosis-0.10502687
Mean773.57036
Median Absolute Deviation (MAD)45.700012
Skewness-0.25251606
Sum796003.9
Variance6434.3352
MonotonicityNot monotonic
2023-07-29T00:58:41.778823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
755.799988 30
 
2.9%
594 30
 
2.9%
670 23
 
2.2%
613 22
 
2.1%
801 16
 
1.6%
887.099976 15
 
1.5%
746.599976 15
 
1.5%
712 14
 
1.4%
845 14
 
1.4%
750 12
 
1.2%
Other values (292) 838
81.4%
ValueCountFrequency (%)
594 30
2.9%
605 5
 
0.5%
611.799988 5
 
0.5%
612 1
 
0.1%
613 22
2.1%
613.200012 2
 
0.2%
614 1
 
0.1%
623 2
 
0.2%
630 5
 
0.5%
631 4
 
0.4%
ValueCountFrequency (%)
992.599976 5
0.5%
945 4
0.4%
943.099976 4
0.4%
942 4
0.4%
925.700012 5
0.5%
905.900024 5
0.5%
903.799988 5
0.5%
903.599976 5
0.5%
901.799988 5
0.5%
900.900024 5
0.5%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.699708
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-07-29T00:58:42.005221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median28
Q356
95-th percentile180
Maximum365
Range364
Interquartile range (IQR)49

Descriptive statistics

Standard deviation63.189113
Coefficient of variation (CV)1.3827028
Kurtosis12.157431
Mean45.699708
Median Absolute Deviation (MAD)21
Skewness3.2678174
Sum47025
Variance3992.864
MonotonicityNot monotonic
2023-07-29T00:58:42.214657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
28 425
41.3%
3 134
 
13.0%
7 125
 
12.1%
56 91
 
8.8%
14 62
 
6.0%
90 54
 
5.2%
100 52
 
5.1%
180 26
 
2.5%
91 22
 
2.1%
365 14
 
1.4%
Other values (4) 24
 
2.3%
ValueCountFrequency (%)
1 2
 
0.2%
3 134
 
13.0%
7 125
 
12.1%
14 62
 
6.0%
28 425
41.3%
56 91
 
8.8%
90 54
 
5.2%
91 22
 
2.1%
100 52
 
5.1%
120 3
 
0.3%
ValueCountFrequency (%)
365 14
 
1.4%
360 6
 
0.6%
270 13
 
1.3%
180 26
 
2.5%
120 3
 
0.3%
100 52
 
5.1%
91 22
 
2.1%
90 54
 
5.2%
56 91
 
8.8%
28 425
41.3%

strength
Real number (ℝ)

HIGH CORRELATION 

Distinct844
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.839446
Minimum2.33
Maximum82.599998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-07-29T00:58:42.413201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.33
5-th percentile10.942
Q123.74
median34.490002
Q346.200001
95-th percentile66.804
Maximum82.599998
Range80.269998
Interquartile range (IQR)22.460001

Descriptive statistics

Standard deviation16.699622
Coefficient of variation (CV)0.46595648
Kurtosis-0.31230574
Mean35.839446
Median Absolute Deviation (MAD)10.970002
Skewness0.41623698
Sum36878.79
Variance278.87739
MonotonicityNot monotonic
2023-07-29T00:58:42.736365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.400002 6
 
0.6%
77.300003 4
 
0.4%
23.52 4
 
0.4%
41.049999 4
 
0.4%
71.300003 4
 
0.4%
31.35 4
 
0.4%
35.299999 4
 
0.4%
79.300003 4
 
0.4%
55.900002 3
 
0.3%
41.540001 3
 
0.3%
Other values (834) 989
96.1%
ValueCountFrequency (%)
2.33 1
0.1%
3.32 1
0.1%
4.57 1
0.1%
4.78 1
0.1%
4.83 1
0.1%
4.9 1
0.1%
6.27 1
0.1%
6.28 1
0.1%
6.47 1
0.1%
6.81 1
0.1%
ValueCountFrequency (%)
82.599998 1
 
0.1%
81.75 1
 
0.1%
80.199997 1
 
0.1%
79.989998 1
 
0.1%
79.400002 1
 
0.1%
79.300003 4
0.4%
78.800003 1
 
0.1%
77.300003 4
0.4%
76.800003 1
 
0.1%
76.239998 1
 
0.1%

Interactions

2023-07-29T00:58:36.538093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:24.743818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:26.162581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:27.575650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:29.060398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:30.574953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:31.993308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:33.474047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:35.148582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:36.679754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:24.905419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:26.306747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:27.761123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:29.238615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:30.716608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:32.143907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:33.616666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:35.296893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:36.855244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:25.055984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:26.456381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:27.919736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:29.400895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:30.896760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:32.309297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:33.806163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:35.451479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:37.016812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:25.212332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:26.613949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:28.084263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:29.561432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:31.052179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:32.480840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:33.970351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:35.611023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:37.172395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:25.356942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:26.788483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:28.241927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:29.708392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:31.201778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:32.631466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:34.136809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:35.759655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:37.339465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:25.503581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:26.929076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:28.404505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:29.861980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:31.365986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:32.816938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:34.320168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:35.905769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:37.503058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:25.649162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:27.102642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:28.563071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:30.021555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:31.517611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:32.972524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:34.534594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:36.063318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:37.678585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:25.807348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:27.263458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:28.735090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:30.181127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:31.676156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:33.146058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:34.801481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:36.221893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:37.841120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:26.008955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:27.416050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:28.886683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:30.417377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:31.838721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:33.312510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:34.974020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-29T00:58:36.385503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-07-29T00:58:42.908910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
cementslagashwatersuperplasticizercoarseaggregatefineaggregateagestrength
cement1.000-0.246-0.419-0.0950.038-0.145-0.1740.0050.478
slag-0.2461.000-0.2550.0540.097-0.348-0.302-0.0190.163
ash-0.419-0.2551.000-0.2830.4530.0600.0520.002-0.079
water-0.0950.054-0.2831.000-0.687-0.219-0.3460.091-0.309
superplasticizer0.0380.0970.453-0.6871.000-0.1970.169-0.0110.347
coarseaggregate-0.145-0.3480.060-0.219-0.1971.000-0.101-0.043-0.182
fineaggregate-0.174-0.3020.052-0.3460.169-0.1011.000-0.057-0.180
age0.005-0.0190.0020.091-0.011-0.043-0.0571.0000.595
strength0.4780.163-0.079-0.3090.347-0.182-0.1800.5951.000

Missing values

2023-07-29T00:58:38.053553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-29T00:58:38.317843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cementslagashwatersuperplasticizercoarseaggregatefineaggregateagestrength
0295.7999880.0000000.000000185.6999970.01091.400024769.2999887.014.840000
1252.3000030.00000098.800003146.30000314.2987.799988889.0000003.021.780001
2172.39999413.600000172.399994156.8000034.11006.299988856.40002428.033.689999
3162.000000214.000000164.000000202.00000010.0820.000000680.00000028.030.650000
4446.00000024.00000079.000000162.00000011.6967.000000712.00000056.055.250000
5540.0000000.0000000.000000173.0000000.01125.000000613.00000014.059.759998
6154.8000030.000000142.800003193.3000039.11047.400024696.70001228.012.460000
7167.399994129.899994128.600006175.5000007.81006.299988746.59997656.051.720001
8277.0000000.0000000.000000191.0000000.0968.000000856.00000014.021.260000
9251.3999940.000000118.300003192.8999945.81043.599976754.299988100.040.150002
cementslagashwatersuperplasticizercoarseaggregatefineaggregateagestrength
1019144.000000136.000000106.0178.0000007.0941.000000774.00000028.026.139999
1020387.00000020.00000094.0157.00000014.3938.000000845.0000003.022.750000
1021349.0000000.0000000.0192.0000000.01047.000000806.000000180.041.049999
1022331.0000000.0000000.0192.0000000.0978.000000825.000000360.041.240002
1023379.500000151.1999970.0153.89999415.91134.300049605.00000091.056.500000
1024210.699997316.1000060.0185.6999970.0977.000000689.29998828.037.810001
1025136.000000196.00000098.0199.0000006.0847.000000783.00000028.026.969999
1026145.0000000.000000134.0181.00000011.0979.000000812.00000028.013.200000
1027376.0000000.0000000.0214.6000060.01003.500000762.40002456.036.299999
1028152.600006238.6999970.0200.0000006.31001.799988683.90002428.026.860001

Duplicate rows

Most frequently occurring

cementslagashwatersuperplasticizercoarseaggregatefineaggregateagestrength# duplicates
1362.600006189.0000000.0164.89999411.6944.700012755.7999883.035.2999994
3362.600006189.0000000.0164.89999411.6944.700012755.79998828.071.3000034
4362.600006189.0000000.0164.89999411.6944.700012755.79998856.077.3000034
5362.600006189.0000000.0164.89999411.6944.700012755.79998891.079.3000034
2362.600006189.0000000.0164.89999411.6944.700012755.7999887.055.9000023
6425.000000106.3000030.0153.50000016.5852.099976887.0999763.033.4000023
7425.000000106.3000030.0153.50000016.5852.099976887.0999767.049.2000013
8425.000000106.3000030.0153.50000016.5852.099976887.09997628.060.2900013
9425.000000106.3000030.0153.50000016.5852.099976887.09997656.064.3000033
10425.000000106.3000030.0153.50000016.5852.099976887.09997691.065.1999973